Dynamic taxonomy process for browsing and retrieving information in large heterogeneous data bases

ABSTRACT

A process is disclosed for retrieving information in large heterogeneous data bases, wherein information retrieval through visual querying/browsing is supported by dynamic taxonomies; the process comprises the steps of: initially showing a complete taxonomy for the retrieval; refining the retrieval through a selection of subsets of interest, where the refining is performed by selecting concepts in the taxonomy and combining them through Boolean operations; showing a reduced taxonomy for the selected set; and further refining the retrieval through an iterative execution of the refining and showing steps.

BACKGROUND OF THE INVENTION

The present invention refers to a dynamic taxonomy process for browsingand retrieving information in large heterogeneous data bases.

Information retrieval on this type of data bases (for example thoseavailable on the Internet) is nowadays a slow task, sometimes impossibleto realize due to the enormous amount of data to be analyzed, and thatcan be implemented with difficulty with the currently available tools.The following documents deal with the prior art in this field: Hearst M.et al: “Cat-a-cone: an interactive interface for specifying searched andviewing retrieval results using a large category hierarchy,” AnnualInternational ACM-SIGIR Conference on Research and Development inInformation Retrieval, US, New York, N.Y.: ACM, 1997, pages 246-255;EP-A-0 694 829 (XEROX Corp.); U.S. Pat. No. 5,644,740 (Kiuchi Itsuko);Gert Schmeltz Pedersen: “A browser for bibliographic informationretrieval, based on an application of lattice theory,” Proceedings ofthe Annual International ACM SIGIR Conference on Research andDevelopment in Information Retrieval, US, New York, ACM, vol. CONF., 16,1993, pages 270-279; and Story G. et al: “The Rightpages image-basedelectronic library for alerting and browsing,” Computer, US, IEEEComputer Society, Long Beach, Calif., US, vol. 25, no. 9, 1 Sep. 1992,pages 17-25.

Dynamic taxonomies are a model to conceptually describe and access largeheterogeneous information bases composed of texts, data, images andother multimedia documents.

The following documents deal with prior art in this field: Hearst M. etal: ‘Cat-a-cone: an interactive interface for specifying searched andviewing retrieval results using a large category hierarchy’, AnnualInternational ACM-SIGIR Conference on Research and Development inInformation Retrieval, US, New York, N.Y.: ACM, 1997, pages 246-255;EP-A-0 694 829 (XEROX Corp.); U.S. Pat. No. 5,644,740 (Kiuchi Itsuko);Gert Schmeltz Pedersen: ‘A browser for bibliographic informationretrieval, based on an application of lattice theory’, Proceedings ofthe Annual International ACM SIGIR Conference on Research andDevelopment in Information Retrieval, US, New York, ACM, vol. CONF., 16,1993, pages 270-279; and Story G. et al: ‘The Rightpages image-basedelectronic library for alerting and browsing”, Computer, US, IEEEComputer Society, Long Beach, Calif., US, vol. 25, no. 9, 1 Sep. 1992,pages 17-25.

As disclosed in Hearst, a dynamic taxonomy is basically a IS-A hierarchyof concepts, going from the most general (topmost) to the most specific.A concept may have several fathers. This is a conceptual schema of theinformation base, i.e. the “intension”. Documents can be freelyclassified under different concepts at different level of abstraction(this is the “extension”). A specific document is generally classifiedunder several concepts.

Dynamic taxonomies enforce the IS-A relationship by containment, i.e.the documents classified under a concept C are the deep extension of C,i.e. the recursive union of all the documents classified under C andunder each descendant C′ of C.

In a dynamic taxonomy, concepts can be composed through classicalboolean operations. In addition, any set S of documents in the universeof discourse U (defined as the set of all documents classified in thetaxonomy) can be represented by a reduced taxonomy. S may be synthesizedeither by boolean expressions on concepts or by any other retrievalmethod (e.g. “information retrieval”). The reduced taxonomy is derivedfrom the original taxonomy by pruning the concepts (nodes) under whichno document d in S is classified.

A new visual query/browsing approach is supported by dynamic taxonomies.The user is initially presented with the complete taxonomy. He/she canthen refine the result by selecting a subset of interest. Refinement isdone by selecting concepts in the taxonomy and combining them throughboolean operations. She/he will then be presented with a reducedtaxonomy for the selected set of documents, which can be iterativelyfurther refined.

The invention described here covers the following aspects of dynamictaxonomies:

1. additional operations;

2. abstract storage structures and operations on such structures for theintension and the extension;

3. physical storage structures, architecture and implementation ofoperations;

4. definition, use and implementation of virtual concepts;

5. definition, use and implementation of time-varying concepts;

6. binding a dynamic taxonomy to a database system;

7. using dynamic taxonomies to represent user profiles of interest andimplementation of user alert for new interesting documents based on suchprofiles of interest.

The above and other objects and advantages of the invention, as willappear from the following description, are obtained by a dynamictaxonomy process as claimed in claim 1. Preferred embodiments andnon-trivial variations of the present invention are claimed in thedependent Claim.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be better described by some preferredembodiments thereof, given as a non-limiting example, with reference tothe enclosed drawing, whose FIG. 1 shows a block diagram of the processof the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Before proceeding with a detailed description of the invention, suitableterminology remarks will be made. The set of documents classified underthe taxonomy (corpus) is denoted by U, the universe of discourse. Eachdocument d in U is uniquely identified by an abstract label calleddocument ID of d (DID(d)). Each concept c in the taxonomy is uniquelyidentified by an abstract label called concept ID of c (CID(c)).Concepts are partitioned into terminal concepts (concepts with noconcept son in the taxonomy) and non-terminal concepts. T denotes theset of concepts used in the taxonomy.

The taxonomy is usually a tree, but lattices (deriving from a concepthaving more than one father) are allowed. Documents can be classifiedunder any (terminal or non-terminal) concept in the taxonomy. A specificdocument d in U may be classified under one or more concepts. Thesingle, most general concept in the taxonomy is called the root of thetaxonomy. This concept need not be usually stored in the extension,since it represents the entire corpus.

The term “deep extension” of a concept c denotes all the documentsclassified under c or under any descendant of c. The term “shallowextension” of a concept c denotes all the documents directly classifiedunder c.

If c is a concept, C^(up)(c) denotes the set {c union {c′: c′ is anancestor of c in the taxonomy, and c′ is not the root of the taxonomy}}.C^(up)(c) is computed by the recursive application of operation AIO3(described hereinbelow). If c is a concept, C^(down)(c) denotes the set{c union {c′: c′ is a descendant of c in the taxonomy}}. C^(down)(c) iscomputed by the recursive application of operation AIO2 (describedhereinbelow).

With reference to FIG. 1, a block diagram is shown of the main steps ofthe process of the present invention, from which all furtherdevelopments of the process itself originate, such developments beingdescribed hereinbelow.

According to the diagram in FIG. 1, the process for retrievinginformation on large heterogeneous data bases of the present inventioncomprises the steps of:

(F1) initially showing a complete taxonomy for retrieval;

(F2) refining the retrieval through a selection of subsets of interest,where the refining step is performed by selecting concepts in thetaxonomy and combining them through boolean operations;

(F3) showing a reduced taxonomy for the selected set; and

(F4) further refining the retrieval through an iterative execution ofthe refining and showing steps.

In addition to the previously-described operations, the followingoperations can be supported:

a. projection under a given CID of a set S of DIDs:

it extracts all the children c of CID such as there is at least adocument in S in the deep extension of c

b. extracting the CID's for a specific document d in U.

The prior art has never specified storage structures nor theimplementation of operations, that are both presented in this context.Abstract storage structures are defined with the following notation.Given domains A1, . . . , AN and B1, . . . , BM:

the relation R: [A1, . . . , AN]→[B1, . . . , BM] means that a N-uple ofvalues drawn from domains A1, . . . , AN uniquely identifies an M-upleof values drawn from domains B1, . . . , BM. If [A1, . . . , AN]→[B1, .. . , BM] holds, then any [A1, . . . , AN]→[Bi] holds, where Bi is drawnfrom any domain in the set {B1, . . . , BM}

the relation R: [A1, . . . , AN]→{B1, . . . , BM} means that a N-uple ofvalues drawn from domains A1, . . . , AN uniquely identifies a set ofM-uples of values drawn from domains B1, . . . , BM. If [A1, . . . ,AN]→{B1, . . . , BM} holds, then any [A1, . . . , AN]→{Bi} holds, whereBi is drawn from any domain in the set {B1, . . . , BM}.

When brackets are omitted in the right part, square brackets areassumed.

Abstract relations can be trivially mapped (for the purpose ofillustration, and with no intent to restrict their representation) torelations in a relational schema, in the following way:

R: R: [A1, . . . , AN]→[B1, . . . , BM] maps into R(A1, . . . , AN, B1,. . . , BM)

R: R: [A1, . . . , AN]→{B1, . . . , BM} maps into a set of 4^(th) NFrelations Ri(A1, . . . , AN, Bi)

where underlined domains are key attributes of R. Abstract SQL querieson these relations will be used to express operations. When expedient,the notation A.B applied to an abstract relation [A]→[B] or [A]→{B} willbe used to denote the value or the set of values of B corresponding to agiven value of A. Domain CID holds the abstract labels of concepts, i.e.stands for the set of values {CID(c), for all c in the taxonomy}. DomainDID holds the abstract labels of documents, i.e. denotes the set ofvalues {DID(d), for all d in U}.

Abstract structures to store the intension will now be described.

The intension is the taxonomy itself; it can be seen as a conceptualschema for a set of corpora. The intension is stored as:

AIS1. One or more “dictionary” relations in the form

Di: [CID]→[textualLabel]

 storing the user-visible definition of each concept; the domain“textualLabel” holds natural language descriptions of concepts. Eachdictionary can be in a different “language”, thereby allowingmultilingual corpora and/or different descriptions of concepts.

AIS2. A language directory, identifying the appropriate dictionaryrelation for a specific “language” (required only if more than one“language” for concept description is used) in the form:

LD: [LANGUAGE_ID]→D

where LANGUAGE_ID holds the abstract identification of languages and Dholds the existing dictionaries.

An alternate representation of AIS1, AIS2 is by a single relation

AIS1′: [CID, LANGUAGE_ID]→textualLabel.

AIS3. A father to son relation in the form

FS: [CID]→{SON_CID}

or

FS′: [CID, SEQ]→[SON_CID]

storing, for each concept c, its sons in the taxonomy. The domainSON_CID is the same as CID. The domain of SEQ is the set of naturalnumbers.

The second form, which is generally used, allows to supply a meaningfuldisplay order among the sons of a concept c.

AIS4. A son to father relation, in the form

SF: [CID]→{FATHER_CID}

 storing, for each concept c, its fathers in the taxonomy. The domainFATHER_CID is the same as CID. If the taxonomy is not a lattice (i.e.any concept c can have no more than one father), this relation becomes:

SF: [CID]→[FATHER_CID].

In this latter case, information on the father of a specific concept cmay alternatively be stored in the dictionaries as:

Di: [CID]→FATHER_CID, textualLabel

although this results in redundancy if more than one dictionary ismaintained.

Abstract storage structures for the extension will now be described.

The extension represents the classification of documents. As such, itdepends on the specific corpus. The extension is abstractly representedby the following three relations:

AES1. Deep extension, in the form

DE: [CID]→{DID}

 storing, for each concept c, all the documents in its deep extension(that is, all the documents classified under c or under any descendantc′ of c).

AES2. Shallow extension, in the form

SE: [CID]→{DID} equivalent to [CID, DID]

 storing, for each concept c, all the documents in its shallow extension(that is, all the documents directly classified under c). The shallowextension and the deep extension are the same for terminal concepts, sothat for such terminal concepts only one of DE and SE needs to be kept(typically, DE will be kept).

AES3. Classification, in the form

CL: [DID]→{CID}

 storing, for each document, the most specific concepts under which itis classified. All the ancestors of these concepts can be easilyrecovered through the son-to-father (SF) relation in the intension. Thisstructure is required only if the display of the classification forstored documents is supported at the user level. This storage structureis optional, since the set K of concepts under which a specific DID isstored can be synthesized by operation AE05 applied to each concept c inT on the singleton set {DID}. A concept c is then in K if and only ifoperation AE05 returns TRUE.

AES4. Document directory

Not specified, since it depends on the host system. It maps a documentid into information required to retrieve the specific document (forexample, the file name).

The abstract implementation of operations on the intension will now bedescribed.

AIO1. Given a concept c identified by K=CID(c), find its label in aspecific language L.

1. Access the appropriate language directory

SELECT D

FROM LD

WHERE LANGUAGE_ID=L

2. Use K as a key to access the textual label

SELECT textualLabel

FROM D

WHERE CID=K

AIO2. Given K=CID(c) find all its sons.

Access the father-to-son relation FS, using K as a partial key

SELECT SON_CID

FROM FS

WHERE CID=K

Or

Access the father-to-son relation FS′, using K as a partial key

SELECT SEQ, SON_CID

FROM FS′

WHERE CID=K

ORDER BY SEQ, SON_CID

AIO3. Given a K=CID(c), find all its fathers.

Access the son-to-father relation SF, using K as a partial key

SELECT FATHER_CID

FROM SF

WHERE CID=K

AIO4. Insert, delete, change operations.

Insert operations are performed by inserting the new concept C:

in the dictionaries (AIS1)

in the father to son relation (AIS3)

in the son to father relation (AIS4)

If C is a son of another concept C′, it may be useful to allow the userto reclassify under C some of the documents presently classified in theshallow extension of C′.

In the case in which each concept has a single father in the taxonomy,the deletion of a concept C is performed by deleting from the intension(AIS1, AIS3, AIS4) all concepts c∈C^(down)(C). In addition (in order toavoid losing documents), the documents in the deep extension of C shouldbe added to the shallow extension of C′, where C′ is the father of C inthe taxonomy, unless C′ is the root of the taxonomy. The shallow (AES2)and deep (AES1) extensions for all concepts c∈C^(down)(C) must beremoved. The concepts in C^(down)(C) must be removed from theclassification (AES3) of all the documents in the deep extension of C.

Alternatively, and in the general case in which concepts can havemultiple fathers, we proceed as follows.

Define LinkDelete(f, s) as:

1. remove from AIS3 the instance where CID=CID(f) and SON_CID=CID(s)

2. remove from AIS4 the instance where CID=CID(s) and FATHER_CID=CID(f)

Define BasicDelete(c) as:

1. for each f in {f: f is a father of c} call LinkDelete(f, c)

2. remove the deep (AES1) and shallow (AES2) extension for c, itsclassification (AES3), and any dictionary entries associated with c.

Define RecursiveDelete(f, s) as:

1. if f is the only father of s then

1.1. for each s′ in {s′: s′ is a son of s} call

RecursiveDelete(s, s′)

1.2. call BasicDelete(s)

2. else call LinkDelete(f, s)

Define RecomputeDeepExtension(c) as:

1. for each s in {s: s is a son of c}

1.1. set the deep extension of c:

DeepExtension(c)=DeepExtension(c) union RecomputeDeepExtension(s)

2. return(DeepExtension(c))

Define UpdateDeepExtension(c) as:

1. for each f in {f: f is a father of c}

1.1. DeepExtension(f)=DeepExtension(c) union ShallowExtension(f)

1.2. UpdateDeepExtension(f)

Deletion of c is then implemented as:

1. Compute the set F(C), which represents all the fathers of the conceptto be deleted (accessible through relation AIS4). All and only theconcepts in F(C) and their ancestors will have their deep extensionaffected by the deletion of C.

2. For each s in {s: s is a son of C}, call RecursiveDelete(C, s)

3. Call BasicDelete(C).

4. Recompute the deep extension of all the fathers of C: for each f inF(C) call RecomputeDeepExtension(f)

5. Update the deep extension of all the ancestors of the set F(C):

5.1. For each f in F(C) call UpdateDeepExtension(f)

Changes in the taxonomy may be of three types:

1. changing the labeling of a concept C: this only requires themodification of the textualLabel in AIS1

2. changing the place of a concept C in the taxonomy

3. adding an additional father C′ to C in the taxonomy

In case 2, let C′ be the current father of C and C″ the new father of C.First, C must be deleted from the taxonomy, and reinserted with C″ as afather. The deep extension of C must be deleted from the deep extensionof all concepts c∈C^(up)(C′) (by set subtraction, or by applying theabove algorithm for deletion with steps 2 and 3 replaced by Creparenting). The deep extension of C must be added to the deepextension of all concepts c∈C^(up)(C″) (by set union). No changes inshallow extensions are required.

In case 3, the deep extension of C must be added to the deep extensionof all concepts c∈C^(up)(C′) (by set union).

The abstract implementation of operations on the extension will now bedescribed.

AEO1. Given a concept c such that CID(c)=K, find its deep extension.

Access the deep-extension relation DE, using K as a partial key

SELECT DID

FROM DE

WHERE CID=K

AEO2. Given a concept c such that CID(c)=K, find its shallow extension.

Access the shallow extension relation SE, using K as a partial key

SELECT DID

FROM SE

WHERE CID=K

AEO3. Test the membership of a set of DIDs {DID} in the deep extensionof a concept CID.

1. Retrieve the deep extension of CID

2. For each d in {DID}, test whether d belongs to the deep-extension; ifit does, return TRUE; if no d in {DID} does, return FALSE

AEO4. Given a set of DIDs {DID}, count the number of documents in {DID}which are also in the deep extension of CID.

1. Retrieve the deep extension of CID

2. Initialize CNT to 0

3. For each d in {DID}, test whether d belongs to the deep-extension; ifit does, CNT=CNT+1

4. Return CNT

AEO5. Test the membership of a set of DIDs {DID} in the shallowextension of a concept CID.

As in AEO3, by substituting the deep extension with the shallowextension.

AEO6. Given a set of DIDs {DID}, produce the projection under a conceptCID.

1. Retrieve the set {SON} of all the sons of CID

2. Initialize set R to empty

3. For each concept s in SON, use operation AEO3, or operation AEO4 ifcounters are desired, to test the membership of {DID} in s. If theoperation returns TRUE (>0 if AEO4 is used) add s to list R

4. Return R

AEO7. Given a set of DIDs {DID}, produce the reduced taxonomy for {DID}.

As a clarification, the set of DIDs for which the reduced taxonomy hasto be produced can be generated by operations on the taxonomy and alsoby any other means, including, without loss of generality, databasequeries and information retrieval queries. Also, the current combinationof concepts can be used as a pre-filter for other retrieval methods.

For performance reason, the reduced taxonomy is usually produced ondemand: the request only displays the highest levels in the tree. Theset {DID} is kept in memory, so that when the explosion of a specificconcept in the reduced taxonomy is requested, appropriate filtering isperformed.

1. Produce the projection of {DID} for the root On the subsequentexplosion of concept c:

Produce the projection of {DID} for c

The reduced tree can also be totally computed in a single step. Let RTbe the set of concepts in the reduced tree. RT can be computed bytesting, for each concept c in T, the membership of {DID} in c throughoperation AEO3 or AEO4 (if counters are required). Concept c is in RT ifand only if operation AEO3 returns TRUE or operation AEO4 returns acounter larger than 0.

The computation can be speeded up in the following way:

1. Initialize a table S of size |T|, where S[i] holds information on thecurrent status of concept i, initialized at “pending”.

2. Starting from the uppermost levels, and continuing down in the tree,process concept i.

2.1. If S[i] is “empty”, i does not belong to RT, and processing cancontinue with the next concept.

2.2. If S[i] is not “empty”, apply operation AEO3 or AEO4 to i.

2.2.1. If the operation returns TRUE (AEO3) or a counter larger than 0(AEO4), i belongs to RT.

2.2.2. Otherwise, neither i nor any of its descendants belong to RT: setto “empty” all S[j] in S, such that j is a descendant of i in thetaxonomy. Descendants can be efficiently obtained by keeping aprecomputed table D, holding for each concept in the taxonomy a list ofall the concepts descending from it in the taxonomy (such a table mustbe recomputed every time the taxonomy changes).

AEO8. Boolean combination of concepts.

Boolean combinations of concepts are performed through the correspondingset operations on the deep extension of concepts. Let c and c′ be twoconcepts, and DE(c) and DE(c′) their deep extension (represented byAES1):

c AND c′ corresponds to DE(C)∩DE(c′)

c OR c′ corresponds to DE(c)∪DE(c′)

c MINUS c′ corresponds to DE(c)−DE(c′)

NOT c corresponds to U-DE(c), where U is the universe

AEO9. Insertion of a new document.

The insertion of a new document d (represented by DID(d)) classifiedunder a set of concepts {C} requires the following steps:

for each c∈{C}

1. insert DID(d) in the shallow extension of c (AES2), if c is not aterminal concept and the shallow extension must be stored

2. insert DID(d) in the deep extension (AES1) of C^(up)(c).

3. insert an item [DID(d)]→{C} in the classification structure AES3

AEO10. Deletion of an existing document.

The deletion of a document d (represented by DID(d)) requires thefollowing steps:

1. retrieve the set of concepts {C} under which d is shallowlyclassified, by accessing AES3 with DID(d) as the key (operation AEO2)

2. for each c∈{C}

a. delete DID(d) from the shallow extension of c

b. for all c′∈C^(up)(c): delete DID(d) from the deep extension of c′

3. delete the entry corresponding to DID(d) from AES3.

If AES3 is not stored, deletion is performed in the following way. Foreach concept c in T, if d belongs to the shallow extension of c:

1. delete DID(d) from the shallow extension of c

2. for all c′∈C^(up)(c): delete DID(d) from the deep extension of c′

AEO11. Document reclassification.

Changes in the classification of a document d (represented by DID(d))are implemented in the following way. Let d be initially classifiedunder a concept c (possibly null) and let the new concept under which dmust be classified be c′ (possibly null). If both c and c′ are non-null,the operation means that d was previously classified under c and mustnow be classified under c′; if c is null, the operation means that d isadditionally classified under c′; if c′ is null, the operation meansthat the original classification under c must be removed. At least oneof c and c′ must be non-null.

If c is not null:

1. eliminate DID(d) from the shallow extension (AES2) of c

2. eliminate DID(d) from the deep extension (AES1) of all c″∈C^(up)(c)

3. eliminate c from the classification of d (AES3)

If c′ is not null:

1. insert DID(d) in the shallow extension (AES2) of c′ (if the shallowextension of c exists)

2. insert DID(d) in the deep extension (AES1) of all c″∈C^(up)(c′)

3. insert c′ in the classification of d (AES3)

AEO12. Find the concepts under which a document d is immediatelyclassified.

Retrieve {C} from AES3, using DID(d) as a key.

Physical storage structures, architecture and implementation ofoperations will now be described.

As regards the intension, storage structures usually contribute with anegligible overhead to the overall storage cost, since a few thousand ofconcepts are usually adequate even for semantically rich corpora.Storage for these structures may be provided by any database managementsystem or any keyed access method. The second form of AIS3 (FS′)requires an ordered access, since SEQ is used to order the sons of aspecific concept. Because of the low overhead, all the intensionalstorage structures (with the possible exception of AIS1, thedictionaries) may be usually kept in central memory.

As regards the extension, the most critical component is AES1 (the deepextension), for several reasons. First, deep-extension semantics are thenatural semantics for boolean combinations of concepts (see AEO8).Second, the production of reduced taxonomies requires a possibly largenumber of projections (which are performed on the deep extension), whoseperformance is critical for visual operations.

It is critical that the deep extension of concept c is explicitlystored, and not computed as the union of the shallow extensions of allthe descendants of c.

Although any dbms or keyed access method can be used to provide storagefor the deep extension, the set of documents in the deep extension canbe more efficiently represented than by straightforwardly mapping theabstract relation.

The use of fixed size bit vectors in the present context will now bedescribed. Information data bases with a small-to-moderate number ofdocuments can effectively represent the deep extension of a concept c bybit vectors, each of size equal to |U′|, the maximum number of documentsin the universe. In the bit vector, bit i is set if and only if thedocument d with DID(d)=i is in the deep extension of c.

Set operations on the deep extension only involve logical operations onbit vectors (AND, OR, NOT, etc.). These operations take one or more bitvectors and produce a result bit vector of the same size.

Let document id's be numbered 0 to |U′|−1, and n be the number of bitsin the word of the host CPU. For performance reasons, it is better toset the fixed size of bit vectors at ┌|U′|/n┐, in order to be able toperform bit operations at the word level. Unused bit positions are leftunset.

Counting the number of documents in the result of any operation can beefficiently performed by table lookup, in the following way.

Let the unit of access UA (not necessarily the CPU word) be n bits.Build once a vector V of 2^(n) elements, stored in memory, which storesin V[i], the number of bits set in the binary number 2^(i),0<=i<=2^(n)−1.

Counting:

Initialize counter C at 0;

Access the bit vector in chunks of n bits at a time:

for each chunk

store the chunk in i

set C=C+V[i]

For access at the octet level (n=8), the translation table requires nomore than 256 octets. For access at the double octet level (n=16), nomore than 64 K octets. Larger units of access are not recommended.

Insertion, deletion and reclassification are also efficiently performed,by simply locating the appropriate deep and/or shallow extension andsetting/resetting the appropriate bit.

This same representation can be trivially used for storing structuresAS2 and AS3. In AS3 the size of the bit vector is equal to thecardinality of the set of concepts in the taxonomy.

As regards compressed bit vectors, by construction, the deep extensionis very sparse at terminal level, and very dense at the top levels inthe taxonomy. The use of any type of bit vector compression (such as,without prejudice to generality, Run Length Encoding (see Capon J., “Aprobabilistic model for run-length coding of pictures”, IEEE Trans. onInf. Theory, 1959) and/or variable-length bit vectors) is thereforebeneficial in reducing the overall storage overhead, although itintroduces a compression/decompression overhead.

If a controlled error-rate in operations is acceptable, Bloom filters(see Bloom, B. H., Space/time tradeoffs in hash coding with allowableerrors, Comm. of the ACM, 1970) can be used to represent the deepextension in a compact form, suitable for larger information bases. WithBloom filters, counting and set negation are usually not supported.

For large to very large information bases, a bit vector representation(albeit compressed) may produce an excessive storage overhead. The deepand shallow extensions as well as structure AES3 may be stored asinverted lists (see Wiederhold, G., Files structures, McGraw-Hill,1987). Because of performance in the computation of set operations, suchlists (and the result of set operations) are kept ordered by documentid's. For the above-cited statements, it is generally advantageous touse any form of inverted list compression.

As regards the general architectural strategies, the implementation ofdynamic taxonomies should try to keep all the relevant data structuresin main memory, shared by the processes accessing them.

As noted before, the intension overhead is generally negligible so thatintensional structures (with the possible exception of dictionaries) maybe usually kept in memory without problems.

Extension overhead for extensional structures is considerably larger. Ifthe storage overhead prevents the complete storage of deep-extensionstructures, buffering strategies should be used, such as LRU or the onesdescribed in documents Johnson, T., Shasha D.: 2Q: A Low Overhead HighPerformance Buffer Management Replacement Algorithm, Int. Conf. on VeryLarge Databases, 1994; and O'Neill, et al.: The LRU-K Page ReplacementAlgorithm For Database Disk Buffering, SIGMOD Conf. 1993. Shallowextensions and classification structures are less critical and may bekept on disk (again with the buffering strategies described in the twoabove-mentioned documents).

As indicated in operation AEO3, the membership test without counting canreturn TRUE when the first DID common to both lists is found, therebyspeeding up the computation.

The use and implementation of virtual concepts will now be described.

Some data domains (such as price, dates, quantities, etc.) correspondusually to a concept (e.g. PRICE) which can be expanded into a largenumber of terminal concepts, each representing a specific value (e.g.100$). Such a representation causes a high number of son concepts, andincreases the complexity of the taxonomy. Alternatively, values can begrouped by defining meaningful intervals of values and representing onlythe intervals as specific concepts. This representation loses the actualdata, and presents the user with a fixed classification. Grouping mayalso be combined with exhaustive representation, but inherits most ofthe problems of both schemes.

The invention of “virtual concepts” provides a third, more flexiblealternative. We define a “Simple virtual concept” as a concept for whichneither the actual sons (actual values of the domain to be represented)nor the actual extension are stored, but are computed (usually fromadditional, possibly external data).

A virtual concept is completely described by 4 abstract operations:

V1: Given a virtual concept v, retrieve all its sons.

V2: Given a virtual concept v, retrieve its deep extension.

V3: Given the son s of a virtual concept v, retrieve its deep extension.

V4: Given a document d, find all the terminal concepts (descendants ofv) under which it is stored.

One way of implementing these abstract operations is by keeping, foreach virtual concept v, two abstract relations:

S_(v): [value]→{DID}

which stores the set of documents with a given value in the domain ofvalues of the virtual concept.

C_(v): [DID]→{value}

which stores the set of values for a specific document; if each documenthas a single value C_(v): [DID]→[value]. A single C_(v) relation maystore multiple domains and be shared by many virtual concepts: in thiscase C_(v): [DID]→{valueA, . . . , valueN}, where valuei denotes the setof values for domain I. It is important to note that neither S_(v) norC_(v) need to be explicitly stored, but they can be also synthesized byqueries on external data.

These two abstract relations can be represented by a single relation ina relational schema (without loss of generality and simply to provide aclear description of operations)

C_(v)(DID, value)

with underscored attributes representing the primary keys. S_(v)actually stores the inversion of C_(v) and will usually be representedby a secondary index on C_(v), rather than by a base relation.

With this representation, the abstract operations defined before can beeasily implemented by SQL queries:

V1: Given a virtual concept v, retrieve all its sons:

SELECT DISTINCT value

FROM C_(v)

V2: Given a virtual concept v, retrieve its deep extension:

SELECT DISTINCT DID

FROM C_(v)

V3: Given the son s of a virtual concept v, retrieve its extension (s isa terminal concept, so that its deep and shallow extension are the same)

SELECT DISTINCT DID

FROM C_(v)

WHERE value=s

Counting is trivially added.

V4: Given a document d, find all the terminal concepts (descendants ofv) under which it is stored

RETRIEVE DISTINCT value

FROM C_(v)

WHERE DID=d

In general, a virtual concept v can be organized into a sub-taxonomy,i.e. each non-terminal son of v represents a set of actual domainvalues. Each son may be further specialized, and so on. For instanceSALARY can be organized into the following taxonomy:

SALARY Low (e.g. <1000) Medium (e.g. >=1000 and <10000) High (e.g.>10000)

In this case, the non-terminal descendants of v can be stored as derivedvirtual concepts, i.e. virtual concepts referencing the same abstractrelations defined for v, but providing additional restrictions. In theexample, “Low” can be characterized by the additional restrictionvalue<1000, so that operation V3 for Low becomes:

SELECT DISTINCT DID

FROM C_(v)

WHERE value<1000

Virtual and derived virtual concepts are peculiar in that their terminaldescendants and their extensions are not directly stored but computed.In order to represent them in our framework, the following abstractrelations are added to the intension:

AIS5: [CID]→[conceptType]

where conceptType designated real, simple virtual and derived virtualconcepts.

AIS6: [CID]→[S_(CID)]

for simple virtual concepts, stores the abstrac relation Sv (which cansynthesized be a query) for the virtual concept CID

AIS7: [CID]→[C_(CID)]

for simple virtual concepts, stores the abstract relation Cv (which cansynthesized be a query) for the virtual concept CID

AIS8: [CID]→[CID′, restriction]

for derived virtual concepts only, identifies the virtual concept torefer to and the additional restriction.

The use and implementation of time-varying concepts will now bedescribed.

Time-varying concepts, such as age, can be represented by a simplevariant of virtual concepts. A time instant t is represented as anabstract “timestamp”. The timestamp contains the number of clock ticksstarting from a fixed time origin; the clock resolution depends on theapplication. All timestamps use the same time coordinates. Thedifference between two timestamps t and t′ defines the time intervalamplitude between the two times. Let the values of the virtual concept vbe the set of timestamps of all documents in the extension of v, and letT be the timestamp of the current time, and the sons of v be representedas time intervals with respect to the current timestamp T:

Given a virtual concept v, retrieve all its sons:

SELECT DISTINCT T-value

FROM C_(v)

Given a virtual concept v, retrieve its deep extension:

SELECT DISTINCT DID

FROM C_(v)

Given the son s of a virtual concept v, retrieve its extension

SELECT DISTINCT DID

FROM C_(v)

WHERE value=T-s

Alternatively, and more efficiently, the values of the time-varyingconcept can be split into N intervals (from more recent to older), whichare stored as real concepts. In addition, for each interval I, we keep:

a. the list L(I) of DIDs in the interval ordered by decreasingtimestamps (i.e. newer to older)

b. in central memory, an interval representative IR(I): the last DID inthe interval together with its timestamp

c. a classification criterion (e.g. T-value less than 1 week and nosmaller than 1 day)

Since the classification of documents varies with time, we need tore-compute the classification of documents every time tick (arbitrarytime interval selected by the system administrator, typically a multipleof clock resolution), according to the following algorithm:

At each time tick:

For each interval I

while IR(I) needs reclassification (i.e. it fails the classificationcriterion for I) do

{ Reclassify(IR(I)); set as IR(I) the last DID in the ordered list a) }where Reclassify(IR(I)) is Delete IR(I).DID from I For(i = i + 1 to N) {if IR(I).timestamp meets the classification criterion for interval i {insert IR(I) in interval i break; } }

Binding a dynamic taxonomy to a database system will now be described.

The present invention allows to use a dynamic taxonomy to browse andretrieve data stored in a conventional dbms (relational,object-relational, object-oriented, etc.). The invention covers datastored as a single relation (or object) or, more generally, representedby a single view on the database (see Elmasri, Navathe, Fundamentals ofdatabase systems, The Benjamin/Cummings Publ. Co., 1994).

In this case documents correspond to tuples (or rows, records, objects)in the view V. In order to identify a document we can either use theprimary key of the view as a document identifier (DID) or keep twoabstract relations mappina system-generated DID's to and from theprimary key PK of the view:

DK: [DID]→[PK]

IDK: [PK]→[DID]

where PK represents the primary key of the relation. DK is used toaccess a tuple of V, given a document id DID, and IDK is used toretrieve the document id corresponding to a specific value in theprimary key of V. This latter representation is beneficial when primarykeys PK's are large (e.g. when they are defined on alphanumericattributes).

Given a view V we can construct a taxonomy T for V in the following way.For each attribute A in V, we place a corresponding concept C(A) (eithera real or a virtual one) as an immediate son of the root. Virtualconcepts use V itself for the synthesis of sons and extensions (aspreviously seen). Real concepts can be further specialized as requiredby the semantics of A.

Given a tuple t in V, for each attribute A in V, let t.A denote thevalue of attribute A in t. For each real concept C in T (either C(A) ora descendant of C (A)), the designer must provide a boolean clause B(C,t) such that t (represented by DID(t)) is to be classified under C ifand only if B(C, t)=TRUE.

The boolean clause B(C, t) may reference any attribute of t, andconsequently, new virtual concepts (called “extended concepts”) may bedefined on combinations of attributes by operations on the database(including but not restricted to sums, averages, etc. of databasevalues).

A special case occurs when the boolean clause B(C, t) is true whent.A∈S_(c), where S_(c) is a set of values of attribute A andS_(c)∩S_(c′)=Ø, for ∀C≠C′. In this case, it is more efficient to keep atable T: [v]→[c], listing for each value v in domain(A), thecorresponding concept c. If S_(c)∩S_(c′)≠Ø, for ∃C≠C′, multiple conceptscan be associated with the same value, so that T: [v]→{c}.

In addition to this mapping among attributes and concepts, the designermay define new concepts either as taxonomic generalizations ofattributes or extended concepts.

New taxonomic generalizations. For virtual concepts, this feature wasdiscussed previously. If the sons of a new taxonomic generalization Gare real concepts {S}, no boolean clause is usually required for G,because classification under G is automatically performed by operationAEO9.

Extended concepts. New concepts may be derived either as real or virtualconcepts by operations on the database (including but not restricted tosums, averages, etc. of database values).

Binding is then performed in the following way. Virtual concepts do notrequire any special processing, since they are realized by operations onthe database. Real concepts require a classification for any new tuple,a deletion if t is deleted or a reclassification if t is changed. Inorder to classify t, the system locates the set C of concepts for whichB(c, t), c∈C is satisfied and classifies t under ∀C∈C (and, consequentlyunder all of c's ancestors). Deletion and reclassification are performedas previously stated.

EXAMPLE

Given the relation R: (TOWNID, NAME, COUNTRY, POPULATION), we canidentify the documents in the database by the values of TOWNID. We needto decide which attributes will be represented in T and how they will berepresented. Let COUNTRY be represented by a real concept, and NAME berepresented by a virtual concept. In addition we define the real conceptCONTINENT as the continent the COUNTRY is in. CONTINENT can berepresented in two ways: as a taxonomic generalization concept or as anextended concept.

If we represent CONTINENT as an extended concept, the taxonomy T willbe:

NAME

Sv:Select TOWNID FROM R WHERE NAME=x

Cv:Select DISTINCT NAME FROM R

CONTINENT

EUROPE t.COUNTRY=“Italy” or t.COUNTRY=“France” or . . .

AMERICA t.COUNTRY=“USA” or . . .

ASIA t.COUNTRY= . . .

COUNTRY Italy t.COUNTRY = “Italy” France t.COUNTRY = “France” USAt.COUNTRY = “USA” . . .

If we represent CONTINENT as a taxonomic generalization of COUNTRY, thetaxonomy T′ will be:

NAME

Sv:Select TOWNID FROM R WHERE NAME=x

CONTINENT EUROPE Italy t.COUNTRY = “Italy” France t.COUNTRY = “France”AMERICA USA . . . . . . ASIA . . . COUNTRY Italy t.COUNTRY = “Italy”France t.COUNTRY = “France” USA t.COUNTRY = “USA” . . .

In both cases, NAME is represented in the same way. For NAME, we havetwo abstract relations

Sv: [COUNTRY]→{TOWNID}

Cv: [TOWNID]→[COUNTRY]

POPULATION is represented in an analogous way.

Finally, the use of dynamic taxonomies to represent user profiles ofinterest and implementation of a user alert for new interestingdocuments based on dynamic taxonomy profiles, will be described.

The invention consists in using set-theoretic expressions on concepts(plus optional, additional expressions, such as information retrievalqueries) to describe user interest in specific topics. Such expressionsmay be directly entered by the user or transparently and automaticallycaptured by the system, by monitoring user query/browsing. Thespecification of user profiles is especially important in electroniccommerce and information brokering and in monitoring dynamic datasources in order to advise users of new or changed relevant information.The information base is assumed to be classified through dynamictaxonomies.

The scenario is as follows. Several users express their intereststhrough possible multiple conceptual expressions, called “interestspecifications”. A monitoring system accepts these requests (with anabstract user “address” to send alerts to). The monitoring system alsomonitors an information base for changes (insertion, deletion, change).The information base is described by the same taxonomy used by users toexpress their interests.

When a change occurs in the information base (the type of change to bealerted for may be specified by users), the system must find the usersto alert on the basis of their interests.

A brute force approach will check all user interest specificationsexhaustively, and compute whether each changed document d satisfies anygiven specification S. We can test whether a document d satisfies aspecification S by applying the query specified in S to the singletonset {d} and test if d is retrieved. However, this strategy requires toperform, for each information base change, as many queries as there areuser specifications and may be quite expensive in practice. For thisreason, we define alternate strategies which reduce the number ofevaluations required.

We are primarily interested into the efficient solution of dynamictaxonomy specifications. Additional expressions, such as informationretrieval queries, will usually be composed by AND with taxonomicexpressions, and can therefore be solved, if required, after thecorresponding taxonomic expression is satisfied.

We will start from the simplest case, in which:

a) the specification is expressed as a conjunction of terminal concepts;

b) documents are classified under terminal concepts only.

As regards conjunctive specifications and document classification underterminal concepts only, we use two abstract storage structures:

1. a directory of specifications, in the form:

SD: [SID]→[N, SPEC]

where SID is an abstract identifier which uniquely identifies thespecification, SPEC is the specification itself (optional), N is thenumber of concepts referenced in the specification. Optionally, otherfields (such as the user “address”) will be stored in this structure.

2. a specification “inversion”, in the form:

SI: [CID]→{SID}

listing for each concept c (represented by its concept identifier) allthe specifications (represented by their specification id) using thatconcept.

When a specification is created, its abstract identifier is created, itsdirectory entry is created in SD and the set of concepts referenced inthe specification are stored in the inversion SI.

When a document d is inserted, deleted or changed, let C be the set ofconcepts (terminal concepts by assumption) under which d is classified.The set of specifications that apply to d are then found in thefollowing way.

Let K be the set of concepts used to classify document d. For eachconcept k in K, let SID(k) be the list of specifications for k(accessible through relation SI) ordered by increasing specificationid's. We define MergeCount(K) as the set composed of pairs (SID, N) suchthat SID is in MergeCount(K) if SID belongs to a SID(k), k in K. If thepair (SID, N) is in MergeCount(K), N counts the number of SID(k)referencing SID. MergeCount(K) can be produced at a linear cost, bymerging the SID(k) lists.

Let S be a set initially empty, which represents the set ofspecifications satisfied by d.

For each pair (SID, N)

retrieve SID.N from SD;

if SID.N=N: S=S union SID

As regards specifications using unrestricted set operations, let S(represented by SID(S)) be a specification. Transform S into adisjunctive normal form (i.e. as a disjunction of conjunctions). Leteach conjunctive clause in S be called a component of S. We denote bySIDi(S) the i-th component of S.

Store the directory of specifications as two abstract relations:

SD (as before, with N omitted)

SCD: [COMPONENT]→[SDI, N], where COMPONENT stores components ofspecifications, COMPONENT.SDI represents the specification id of thespecification S of which COMPONENT is a component, and COMPONENT.N isthe number of concepts referenced in the component.

The specification inversion is stored as: SI: [CID]→{COMPONENT}, whereCID is a concept identifier and CID.COMPONENT is the set of componentsreferencing the concept identified by CID.

Let K be the set of concepts used to classify document d, for eachconcept k in K, let COMPONENT(k) be the list of components for k(accessible through relation SI) ordered by increasing component id's.Define ComponentMergeCount(K) as the set composed of pairs (COMPONENT,N) such that COMPONENT is in ComponentMergeCount(K) if COMPONENT belongsto a COMPONENT(k), k in K. If the pair (COMPONENT, N) is inComponentMergeCount(K), N counts the number of COMPONENT(k) referencingCOMPONENT. ComponentMergeCount(K) can be produced at a linear cost, bymerging the COMPONENT(k) lists.

Let S be a set initially empty.

For each pair (COMPONENT, N), retrieve COMPONENT.N through relation SCD;

if COMPONENT.N=N: S=S union COMPONENT.SID (COMPONENT.SID is accessedthrough relation SCD).

S represents the set of specifications satisfied by d.

As regards specifications and document classification under non-terminalconcepts to which they refer, the specification inversion SI needs to bemodified in the following way.

If a specification or component Z references concept C, represented byCID(C) then:

C is a terminal concept:

CID(C).SID=CID(C).SID union Z, if Z is a specification

CID(C).COMPONENT=CID(C).COMPONENT union

Z, if Z is a component

C is a non-terminal concept:

for each k in C^(down)(C)

CID(k).SID=CID(k).SID union Z, if Z is a specification

CID(k).COMPONENT=CID(k).COMPONENT union

Z, if Z is a component

The set S of satisfied specifications is computed as per the previouscases.

The above-disclosed techniques allow computing the specificationssatisfied by a document d. In case it is desired to determine thespecifications satisfied by a set of documents D (whose cardinality isgreater than 1), the above-disclosed techniques can be applied in twoways. In the first way, the techniques are applied without modificationsto every document d in D, then removing possible duplicatespecifications. In the second way, K is defined as the set of conceptsused to classify D, the adequate technique is chosen among the describedones and the set S of “candidate” specifications is determined. Everyspecification s in S is then checked, performing it on D.

What is claimed is:
 1. A process for retrieving information on largeheterogeneous databases, wherein information retrieval is performedthrough visual queries on dynamic taxonomies, said dynamic taxonomiesbeing an organization of concepts that ranges from a most generalconcept to a most specific concept, said concepts and theirgeneralization or specialization relationships being an intension,documents in said databases being able to be classified under differentconcepts, said documents and their classification being called anextension, said process comprising: initially displaying a completetaxonomy for said retrieval; selecting subsets of interest of saidcomplete taxonomy in order to refine said retrieval, said subsets ofinterest being specified by selecting taxonomy concepts and combiningthem through boolean operations or being specified through queryingmethods, which retrieve classified documents according to differentselection criteria, including words contained in a document; displayinga reduced taxonomy for said selected set, said reduced taxonomy beingderived from the original taxonomy by pruning the concepts under whichno document in the selected subset of interest is classified; anditeratively repeating said steps of selecting subsets and of displayinga reduced taxonomy to further refine said retrieval, wherein: saidprocess is performed on documents of any type and format; said intensionis organized as a hierarchy of concepts or as a directed acyclic graphof concepts, thereby allowing a concept to have multiple fathers; saidprocess dynamically reconstructs all relationships among concepts basedon the classification without requiring, in the intension, conceptrelationships in addition to generalization or specialization, arelationship between any two concepts existing if and only if at leastone document is classified (1) under a first concept or any descendantsof the first concept, and (2) under a second concept, or any descendantsof the second concept; documents in said classification are classifiedunder a concept at any level in said intension, including concepts withno sons; said taxonomy supports operations for concept insertion,deletion, and modification; said taxonomy supports operations fordocument insertion and classification, deletion, and reclassification;documents in said classification are classified manually,programmatically, or automatically; said process allows retrievalthrough different languages on a same database, while maintaining thesame classification for all said languages; said classification isexplicitly stored as a set or list of documents for each concept orimplicitly stored in external structures; said explicitly storedclassification includes, for each concept in the intension, deepclassification, which records all documents classified under the conceptand under any of its descendants, and a shallow classification, whichrecords all the documents classified directly under the concepts, saidshallow classification being only required if documents can beclassified under non-terminal concepts and is equivalent, by definition,to the deep classification for terminal concepts; said deep and shallowclassifications are physically stored as compressed or uncompressed bitvectors, or as compressed or uncompressed inverted lists, or as Bloom'sfilters, or in a relational database system; said process accounts foran age of documents either explicitly or implicitly, or by a lazyreclassification of a minimum number of concepts; said intension andclassification are used either for querying/browsing the database or todynamically inform a user when documents of interest are added ormodified in the database; and said step of displaying a reduced taxonomyeither reports only the concepts belonging to the reduced taxonomy or,for each such concept, also reports how many documents in the interestset are classified under the concept.
 2. The process according to claim1, wherein each document d is identified by a unique identifier (DID(d)), and each concept c is identified by a unique identifier (CID (c)),and a conceptual schema for the intension comprises: a dictionaryrelation for each language supported, which binds each conceptidentifier to a label describing that concept, which will be presentedto the user when the taxonomy is displayed; a father-to-son relationwhich lists, for each concept, all sons of the concept, said list beingconfigurable as to be ordered; and a son-to-father relation which lists,for each concept, all fathers of the concept; and a conceptual schemafor the extension comprises, for each concept: a deep classificationthat lists all documents classified under the concept or any descendantsof the concept; a shallow classification, which lists all documentsdirectly classified under the concept; and a classification relationwhich lists, for each document, all concepts under which the document isdirectly classified, ancestors of said concepts being recoverablethrough the son-to-father relation in the intension.
 3. The processaccording to claim 1, wherein boolean operations on concepts areimplemented through corresponding set operations on the deepclassification of said concepts.
 4. The process according to claim 1,wherein said step of displaying a reduced taxonomy for the set selectedin said selecting step comprises a testing operation such that a conceptis displayed in the reduced taxonomy if an intersection between the setand the deep classification of the concept is not empty, the testingoperation being configured to optionally count a number of documents insaid intersection to show a user a number of documents in the set thatare also classified under the concept, said testing operation being alsoconfigured to be applied to the shallow classification, if used, to showthe user a number of documents in the set that are also directlyclassified under the concept, the numbers being useful when documentscan be classified at any level of abstraction in the taxonomy, saidtesting operation being also configured to be applied to a set includinga single document d, in order to compute a classification of d, if notexplicitly stored, said testing operation being used to produce areduced tree by testing and displaying the sons of a root and, onsubsequent explosion of a concept c, testing and displaying the sons ofconcept c.
 5. The process according to claim 1, wherein said taxonomysupports modification operations on the intension, such operationssupporting: an insertion of a new concept, performed by inserting thenew concept in the dictionaries, in the father-to-son relation, and inthe son-to-father relation; a deletion of an existing concept C,performed by: deleting from the intension all concepts in C^(down)(C),wherein C^(down)(C) denotes a set of all descendants of the concept C,in union with the concept C; adding documents in the deep classificationof the concept C to the shallow classification of a set of the fathersof the concept C in the taxonomy; removing the shallow and deepclassifications for all concepts in C^(down)(C); removing the conceptsin C^(down)(C) from a classification of all documents in the deepclassification of the concept C; changing a labeling of a concept, saidchanging step only requiring a modification of an appropriatedictionary; changing a place of a concept in the taxonomy; and adding anadditional father to a concept in the taxonomy.
 6. The process accordingto claim 1, wherein said taxonomy supports modification operations onthe classification, such operations supporting: an insertion of a newdocument d, said insertion comprising the steps of: for each c∈{C},wherein C denotes a set of concepts under which d must be classified,inserting DID(d) in the shallow classification of c, if c is not aterminal concept and the shallow classification must be stored;inserting DID(d) in the deep classification of C^(up)(c), C^(up)(c)denoting a set of all ancestors of the concept c in union with c; andinserting c in a classification of d; a deletion of an existing documentd, said deletion comprising the steps of: retrieving a set of concepts{C} under which d is shallowly classified; for each c∈{C}, deletingDID(d) from the shallow classification of c; for each ancestor of c, cincluded, deleting DID(d) from the deep classification of each ancestor;deleting an entry corresponding to DID(d) from the classificationrelation; a reclassification of an existing document d, according to adifferent concept, said reclassification comprising the steps of:letting d be initially classified under a concept c, possibly null, c′being a new concept under which d must be classified, possibly null; ifboth c and c′ are non-null, classifying d under c′; if c is null,additionally classifying d under c′; if c′ is null, removing theoriginal classification under c; if c is not null, eliminating DID(d)from the shallow classification of c; eliminating DID(d) from the deepclassification of all ancestors c″ of c, c included; eliminating c fromthe classification of d; if c′ is not null, inserting DID(d) in theshallow classification of c′, if the shallow classification of c exists;inserting DID(d) in the deep classification of all ancestors c″ of c, cincluded; inserting c′ in the classification of d.
 7. The processaccording to claim 1, wherein said deep and shallow classifications arephysically stored as uncompressed or compressed bit vectors, and acounting of documents in a result of logic operations on bit vectors isperformed through a constant table V whose size is 2^(n), whose elementV[i] contains a number of bits at 1 in binary number i, and processingthe uncompressed form of the bit vector n bits at a time, adding to acounter for every group j of n bits, whose binary value is v′, an amountV[v′].
 8. The process according to claim 1, wherein said classificationis implicitly stored as virtual concepts in external structures, saidvirtual concepts being concepts for which neither actual sons, that areactual values of a domain to be represented, nor an actualclassification are stored, but instead are computed, said virtualconcept being able to be a simple virtual concept, which is completelydescribed by four abstract operations: V1: given a virtual concept v,retrieve all its sons; V2: given a virtual concept v, retrieve its deepclassification; V3: given the son s of a virtual concept v, retrieve itsdeep classification; and V4: given a document d, find all the terminalconcepts, descendants of v, under which it is classified; said abstractoperations being implemented based on two abstract relations, for eachvirtual concept v: a relation S_(v) which stores a set of documents foreach value in a domain of values of the virtual concept; and itsinversion C_(v), optionally stored, which stores a set of values foreach document classified under said virtual concept; said virtualconcept being configured to be a derived virtual concept, which refersto said two abstract relations, with additional restrictions; additionalinformation being kept in the intension, for each concept, to describeits type; for each simple virtual concept to address said S, and saidoptional C_(v) relation; for each derived virtual concept to addresssaid two abstract relations and a restriction to apply to its baserelations (S_(v) and C_(v)).
 9. The process according to claim 8,wherein said process accounts for an age of documents implicitly bymeans of virtual concepts, or by a lazy reclassification of a minimumnumber of concepts, a representation of time-varying concepts by virtualconcepts being characterized in that the time-varying concepts, whosevalue is represented by abstract timestamps, can be represented by avirtual concept, representing with T the timestamp value of the currenttime, sons of said time-varying concept V being retrieved through anabstract query that selects all unique T-values in C_(v) where T is acurrent time, the deep classification of said time-varying concept Vbeing retrieved through an abstract query that selects all unique DIDsfrom C_(v), the classification of a son s of said time-varying concept Vbeing retrieved through an abstract query that selects all unique DIDsin C_(v) with value=T-s, where T is the current time, in the lazyevaluation of time-varying concepts, the time-varying concepts beingsplit into N intervals, from more recent to older, which are stored asreal concepts, for each interval I, the following being kept: (a) a listL(I) of DIDs in an interval ordered by decreasing timestamps, (b) aninterval representative of IR(I), a last DID in the interval togetherwith its timestamp, (c) a classification criterion for the interval, theclassification of documents being periodically recomputed, for eachinterval I, by reclassifying IR(I) if IR(I) needs reclassification andsetting IR(I) as the last DID in the ordered list (a), and deletingIR(I).DID from I, while iteratively inserting IR(I) in interval i+1 to Nif IR(I) meets a classification criterion for interval i.
 10. Theprocess according to claim 1, wherein said dynamic taxonomy is used torepresent data represented by a single view on an external database, thedocuments corresponding to tuples, rows, records, or objects, in a viewV, and, in order to identify a document, a candidate key of the viewbeing used as document identifier (DID) or two abstract relations beingkept for mapping system-generated DIDs to and from a primary key PK ofthe view, a taxonomy T for V being able to be constructed by insertingconcepts of interest for V in the taxonomy, each concept C beingassociated to a boolean clause B(C, t), where t denotes a tuple, saidboolean clause being able to reference any attribute of t and returningtrue if and only if t must be classified under C, said concept C being areal concept or a virtual concept, said virtual concept using V itselffor a synthesis of sons and extensions, wherein if said real conceptrepresents an attribute A of V such that said boolean clause is truewhen t.A∈S_(c), where S_(c) is a subset of the domain of attribute A,the boolean clause is replaced, for a better performance, by a tablelisting, for each value v in domain(A), corresponding concepts underwhich t is to be classified, in order to create new taxonomicgeneralizations, if the sons of a new taxonomic generalization G arereal concepts, no boolean clause for G being needed, a classificationunder G being automatically performed by an insertion operation for anew document, a binding for real concepts requiring an insertion for anynew tuple, a deletion if t is deleted or a reclassification if t ischanged, said process, in order to classify t, locating a set C ofconcepts for which B(c, t), c∈C is satisfied and classifying t under∀c∈C, and binding for virtual concepts is realized by operations of Vitself.
 11. The process according to claim 1, wherein dynamic taxonomiesare used in order to represent user profiles of interest to alert usersfor new interesting documents based on dynamic taxonomy profiles, saidprocess further comprising the steps of: acquiring multiple conceptualexpressions from a user, said conceptual expressions defining subjectsin which the user is interested; accepting said conceptual expressionsby a monitoring system; coupling, by said monitoring system, an abstractuser address, to which alerts are to be sent, to said conceptualexpressions; monitoring, by said monitoring system, an information basefor changes performed thereto, said information base being described bythe same taxonomy used by users to express their interests; when achange occurs in the information base, finding, by said monitoringsystem, the users to be alerted based on their interests; dynamictaxonomy concepts being used to realize said monitoring step, for saiddynamic taxonomy concepts additional expressions, being composed by ANDwith taxonomic expressions, and being solved, if required, after acorresponding taxonomic expression is satisfied, a checking step whethera document d satisfies a user specification S being performed byapplying a query specified in S to a set {d} and checking whether d isretrieved, if specifications only comprise conjunction operations anddocument classification is only under terminal concepts, two abstractstorage structures being used: a directory of specifications (SD)relating SID and N, SPEC, where SID is an abstract identifier whichuniquely identifies the specification, SPEC is the specification itself,N is a number of concepts referenced in the specification; aspecification inversion (SI), relating CID and SID, listing for eachconcept, represented by its concept identifier, all specificationsrepresented by their specification ID using that concept wherein, when aspecification is created, its abstract identifier is created, itsdirectory entry being created in SD and a set of concepts referenced inthe specification being stored in an inversion SI, while, when adocument d is changed, C being a set of concepts under which d isclassified, a set of specifications that apply to d being found asfollows: K being a set of concepts used to classify document d, for eachconcept k in K, SID(k) is a list of specifications for k, accessiblethrough relation SI, ordered by increasing specification ID's; definingMergeCount(K) as a set composed of pairs (SID, N) such that SID is inMergeCount (K) if SID belongs to a SID (k), k in K; if a pair (SID, N)is in MergeCount(K), N counts a number of SID(k) referencing SID; if Sis an initially empty set, which represents a set of specificationssatisfied by d, for each pair (SID, N), retrieving SID.N from SD; ifSID.N=N: S=S union SID, when there are specifications using unrestrictedset operations, S, represented by SID(S), being a specification and thefollowing steps being used: transforming S in disjunctive normal form asa disjunction of conjunctions, each conjunctive clause in S being calleda component of S, SIDi(S) denoting the i-th component of S; storing adirectory of specifications as two abstract relations: SD, omitting N,and SCD, relating COMPONENT and SDI, N, where COMPONENT storescomponents of specifications, COMPONENT.SDI represents a specificationID of specification S of which COMPONENT is a component, and COMPONENT.Nis a number of concepts referenced in the component; storing aspecification inversion as relation (SI) between CID and CID.COMPONENT,where CID is a concept identifier and CID.COMPONENT is a set ofcomponents referencing the concept identified by CID; with K being theset of concepts used to classify document d, for each concept k in K,COMPONENT (k) is a list of components for k, accessible through relationSI, ordered by increasing component ID's; definingComponentMergeCount(K) as a set composed of pairs (COMPONENT, N) suchthat COMPONENT is in ComponentMergeCount(K) if COMPONENT belongs to aCOMPONENT(k), k in K; if a pair (COMPONENT, N) is inComponentMergeCount(K), N counting the number of COMPONENT(k)referencing COMPONENT; with S being a set initially empty, for each pair(COMPONENT, N), retrieving COMPONENT.N through relation SCD; ifCOMPONENT.N=N: S=S union COMPONENT.SID, S representing a set ofspecifications satisfied by d; the modification of a specificationinversion SI comprising the steps of: if a specification or component Zreferences concept C, represented by CID(C) then:  if C is a terminalconcept:  CID(C).SID=CID(C).SID union Z, if Z is a specification, CID(C).COMPONENT=CID(C).COMPONENT union Z, if Z is a component  if C isa non-terminal concept, for each k in C^(down)(C):  ClD(k).SID=CID(k).SID union Z, if Z is a specification, CID(k).COMPONENT=CID(k).COMPONENT union Z, if Z is a component, thespecifications satisfied by a set of documents D whose cardinality isgreater than 1 being computed by applying previous techniques withoutmodifications to every document d in D, then removing possible duplicatespecifications; or being computed by: defining as K a set of conceptsused to classify D; applying an adequate technique among the describedones; and determining a set S of candidate specifications, everyspecification s in S being then checked by performing it on D.
 12. Theprocess according to claim 1, wherein the reduced taxonomy is totallycomputed in a single step, wherein RT is a set of concepts in thereduced taxonomy, RT being computed by applying said testing operation,for each concept C in the intension, and further in that said operationis speeded up through the steps of: initializing a table S of size T,where T is a number of concepts in the intension and S[i] holdsinformation on a current status of concept i, initialized at pending;starting from uppermost levels, and continuing down in a tree,processing concept i; if S[i] is empty, determining that i does notbelong to RT, and continuing the processing with a next concept; if S[i]is not empty, applying said testing operation to i; if said testingoperation produces a non-empty intersection, determining that i belongsto RT; otherwise, determining that neither i nor any of its descendantsbelong to RT and setting to empty all S[j] in S, such that j is adescendant of i in the taxonomy, the descendants of I being eithercomputed from the intension or being precomputed and stored in a tableD, holding for each concept in the taxonomy a list of all conceptsdescending from it in the taxonomy, such a table being recomputed everytime the intension changes.